 Bootstrap methods assume that our sample is an SRS and will give trustworthy conclusions only if this condition is met.

These methods make no assumptions about the distribution your data comes from.
 For inference for a single population mean, singleparameter bootstrap tests are an alternative to the onesample $t$confidence interval when the guidelines for its use are not met (such as when the data is strongly skewed or has outliers with low sample size).
 For inference for a single population proportion, singleparameter bootstrap tests are an alternative to the onesample $z$confidence interval when the guidelines for its use are not met, that is when the number of successes and failures are not large enough.
Data  Frequencies  
Sample data goes here (enter numbers in columns):  
Number of Successes  Sample Size  
Calculate Interval for a:  
Level of Confidence:  
Number of Bootstrap Samples:  
Input data as Frequency Table: 
Population Mean
Sample Size:  $n=$ 
Sample Mean:  $\overline{x}=$ 
Confidence Interval for the True Mean:  
Frequency  
Sample Data  Bootstrap Means $\mu^*$ 
Population Proportion
Sample Size:  $n=$ 
Sample Proportion:  $\hat{p}=$ 
Confidence Interval for the True Proportion:  
Frequency  
Failures and Successes  Bootstrap Proportions $p^*$ 
Population Median
Sample Size:  $n=$ 
Sample Median:  $M=$ 
Confidence Interval for the True Median:  
Frequency  
Sample Data  Bootstrap Medians $M^*$ 
Population Standard Deviation
Sample Size:  $n=$ 
Sample Standard Deviation:  $s=$ 
Confidence Interval for the True Standard Deviation:  
Frequency  
Sample Data  Bootstrap Standard Deviations $\sigma^*$ 